| 石威,李昕泽,黄文昌,王宁浩,焦阳,崔崤峣.基于改进MultiResUNet网络的甲状腺超声图像分割[J].声学技术,2022,41(2):228~234 |
| 基于改进MultiResUNet网络的甲状腺超声图像分割 |
| Thyroid ultrasound image segmentation based on an improved MultiResUNet network |
| 投稿时间：2021-02-01 修订日期：2021-03-16 |
| DOI：10.16300/j.cnki.1000-3630.2022.02.012 |
| 中文关键词: 甲状腺 超声 Octave卷积 MultiResUNet |
| 英文关键词: thyroid ultrasound Octave convolution MultiResUNet |
| 基金项目:国家自然科学基金（51805529）资助项目。 |
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| 摘要点击次数: 550 |
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| 中文摘要: |
| 甲状腺超声图像分割在临床超声图像研究中有很重要的意义。针对甲状腺超声图像信噪比低，斑点噪声多，且甲状腺形态不确定等问题，提出了一种改进的MultiResUNet分割网络(称为Oct-MRU-Net网络)。该方法在MultiResUNet网络的基本结构的基础上引入Octave卷积，并采用改进的Inception模块学习不同空间尺度的特征，将训练过程中的特征图按通道方向分为高低频特征。其中，高频特征描述图像细节和边缘信息，低频特征描述图像整体轮廓信息。在甲状腺超声图像分割过程中可以重点关注高频信息，减少空间冗余，从而实现对边缘更加精细的分割。实验结果表明，Oct-MRU-Net网络的性能相较于U-Net网络和MultiResUNet网络都有较大的提升，说明该网络对甲状腺超声图像的分割效果较好。 |
| 英文摘要: |
| The ultrasound image segmentation of thyroid is very important in clinical ultrasonography. In view of the problems of the low signal-to-noise ratio, high speckle noise and uncertain thyroid morphology in thyroid ultrasound images, an improved MultiResUNet segmentation network, named as Oct-MRU-Net, is proposed by combining the basic structure of MultiResUNet network and Octave convolution. And, the improved Inception module is used to learn the features of different spatial scales. The feature map in the training process is divided into high and low frequency features according to the channel direction, in which the high frequency features describe the image details and edge information and the low frequency features describe the overall image contour information. Then, the method of ultrasound image segmentation can focus on high-frequency information to reduce spatial redundancy, so more precise edge segmentation can be achieved. The experimental results show that the performance of Oct-MRU-Net network is better than that of U-Net network and MultiResUNet network, and so the Oct-MRU-Net network has better segmentation effect on thyroid ultrasound images. |
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